Forecasting Sea-Level Trends over the Persian Gulf from Multi-Mission Satellite Altimetry Using Machine Learning

Hamzah Tahir, Ami Hassan Md Din, Thulfiqar S. Hussein, Zaid H. Jabbar

One of the most significant impacts of climate change is sea-level rise, which is increasingly threatening to the coastal setting, infrastructure, and socioeconomic systems. Since a change at the sea level is spatially non-uniform and highly modulated by local oceanographic and climatic events, local or regional-scale measurements are necessary—especially in semi-enclosed basins. This paper examines the long-term variability of sea levels throughout the Persian Gulf and illustrates a strong spatial variance of the trends over the past and the future. Using three decades of satellite-derived observations, regional sea-level trends were estimated from monthly sea-level anomaly (SLA) data, which were also used to generate future projections to 2100. The analysis shows that the rate of sea-level rise along the UAE–Oman stretch is 3.88 mm year−1 and that of the Strait of Hormuz is 5.23 mm year−1, with a mean of 4.44 mm year−1 in the basin. Statistical forecasts of sea-level change were projected by a statistical forecasting scheme with high predictive ability with the optimal configuration of an average of 0.0391 m, an RMSE of 0.0492 m, and an R2 of 0.80 when independent validation was conducted. It is estimated that by 2100, the average rise of the sea level in the Persian Gulf is about 0.30–0.40 m, and the peak rise in sea level is at the Strait of Hormuz. Since these projections are based on statistical extrapolation rather than physics-based climate models, they are interpreted within the uncertainty envelope defined by IPCC AR6 scenarios. This study presents a unique, regionally resolved viewpoint on sea-level rise that is relevant to coastal risk management and adaptation planning in semi-enclosed marine basins by connecting robust statistical performance with physically interpretable regional patterns.